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Chapter 17 - NeuralMechanisms Underlying Value-Based Decision Making
- from Section IV - Cognitive-Emotion Interactions
- Edited by Jorge Armony, McGill University, Montréal, Patrik Vuilleumier, Université de Genève
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- Book:
- The Cambridge Handbook of Human Affective Neuroscience
- Published online:
- 05 February 2013
- Print publication:
- 21 January 2013, pp 401-416
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Summary
This chapter explains how human beings infer emotional meaning from vocal signals. It reviews research on cerebral processes that contribute to the decoding of emotions from vocal cues such as speech prosody or nonverbal vocalizations like laughter. With the advent of modern brain imaging techniques, research has achieved substantial progress in delineating the neurobiological bases of (emotional) voice perception. In particular, functional magnetic resonance imaging (fMRI) has contributed greatly to the understanding of how the brain processes emotional information in human voices. Despite the methodological limitations, however, empirical evidence and hypotheses reviewed in the chapter suggest the idea of two distinct modes of speech prosody processing, each implemented differently in the human brain: explicit processing and implicit processing. Suppression of limbic activation reflects a recruitment of emotion regulation processes that attenuate the automatic induction of emotional reactions associated with limbic activation in order to avoid emotional interference in goal-directed behavior.
List of Contributors
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- By Adam K. Anderson, Jorge Armony, Anthony P. Atkinson, Sonia Bishop, Carolin Brück, Roberto Cabeza, Frances S. Chen, Hugo D. Critchley, Mauricio R. Delgado, Ricardo de Oliveira-Souza, Gregor Domes, Judith Domínguez-Borràs, Joseph E. Dunsmoor, Thomas Ethofer, Dominic S. Fareri, Lesley K. Fellows, Sophie Forster, Katherine Gardhouse, Nathalie George, Jay A. Gottfried, Jung Eun Han, Ahmad R. Hariri, Neil A. Harrison, Markus Heinrichs, Alisha C. Holland, Andreas Keil, Elizabeth A. Kensinger, Johanna Kissler, Olga Klimecki, Stefan Koelsch, Sylvia D. Kreibig, Benjamin Kreifelts, Robert Kumsta, Kevin S. LaBar, Eamon J. McCrory, Aprajita Mohanty, Jorge Moll, John P. O’Doherty, Leticia Oliveira, Mirtes Pereira, Luiz Pessoa, K. Luan Phan, Pierre Rainville, David Sander, Annett Schirmer, Catherine L. Sebastian, Tania Singer, Chandra Sekhar Sripada, Peggy L. St. Jacques, Essi Viding, Patrik Vuilleumier, Dirk Wildgruber, Amy Winecoff, Roland Zahn
- Edited by Jorge Armony, McGill University, Montréal, Patrik Vuilleumier, Université de Genève
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- Book:
- The Cambridge Handbook of Human Affective Neuroscience
- Published online:
- 05 February 2013
- Print publication:
- 21 January 2013, pp xi-xii
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The Effect of the Neurogranin Schizophrenia Risk Variant rs12807809 on Brain Structure and Function
- Emma J. Rose, Derek W. Morris, Ciara Fahey, Ian H. Robertson, Ciara Greene, John O'Doherty, Fiona N. Newell, Hugh Garavan, Jane McGrath, Arun Bokde, Daniela Tropea, Michael Gill, Aiden P. Corvin, Gary Donohoe
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- Journal:
- Twin Research and Human Genetics / Volume 15 / Issue 3 / June 2012
- Published online by Cambridge University Press:
- 15 June 2012, pp. 296-303
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- Article
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A single nucleotide polymorphism rs12807809 located upstream of the neurogranin (NRGN) gene has been identified as a risk variant for schizophrenia in recent genome-wide association studies. To date, there has been little investigation of the endophenotypic consequences of this variant, and our own investigations have suggested that the effects of this gene are not apparent at the level of cognitive function in patients or controls. Because the impact of risk variants may be more apparent at the level of brain, the aim of this investigation was to delineate whether NRGN genotype predicted variability in brain structure and/or function. Healthy individuals participated in structural (N = 140) and/or functional (N = 36) magnetic resonance imaging (s/fMRI). Voxel-based morphometry was used to compare gray and white matter volumes between carriers of the non-risk C allele (i.e., CC/CT) and those who were homozygous for the risk T allele. Functional imaging data were acquired during the performance of a spatial working memory task, and were also analyzed with respect to the difference between C carriers and T homozygotes. There was no effect of the NRGN variant rs12807809 on behavioral performance or brain structure. However, there was a main effect of genotype on brain activity during performance of the working memory task, such that while C carriers exhibited a load-independent decrease in left superior frontal gyrus/BA10, TT individuals failed to show a similar decrease in activity. The failure to disengage this ventromedial prefrontal region, despite preserved performance, may be indicative of a reduction in processing efficiency in healthy TT carriers. Although it remains to be established whether this holds true in larger samples and in patient cohorts, if valid, this suggests a potential mechanism by which NRGN variability might contribute to schizophrenia risk.
3 - Decisions, risk and the brain
- Edited by Layla Skinns, University of Cambridge, Michael Scott, University of Cambridge, Tony Cox, University of Cambridge
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- Book:
- Risk
- Published online:
- 05 June 2012
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- 01 September 2011, pp 34-61
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Summary
The ability to make good decisions about future courses of action under conditions of uncertainty is essential for the survival of most animals, including humans. Whether it is deciding which item to choose from a restaurant menu, when to cross a busy road or what career path to follow, we are constantly faced with the need to make decisions of varying degrees of importance in terms of their implications for our future well-being. Often the outcomes of such decisions are highly uncertain, and we must therefore take into account not only the pros and cons of the outcomes associated with different courses of action but also the uncertainties or ‘risk’ attached to such outcomes. On the whole, humans are rather good at making decisions, as exemplified by our incredible success as a species. The root of that success necessarily lies in the machinery contained in our brain, a highly efficient computer weighing approximately 1.36 kg that has been shaped by evolution to allow us the flexibility to make good decisions in diverse and rapidly changing environments. In this chapter, I will give a broad introduction to a new interdisciplinary field of study called ‘neuroeconomics’, which is concerned with elucidating how the brain is capable of enabling us to make such good decisions. I will outline our current understanding about how decisions are made by the brain, and I will highlight some of the outstanding questions for future research in this still nascent field of study.
Neuroeconomics
The field of neuroeconomics has emerged through a fusion of approaches found in more traditional disciplines. These include not only neuroscience and economics, as one might have guessed from the perhaps clumsily put-together title, but also cognitive and behavioural psychology, computer science and artificial intelligence, engineering, robotics and behavioural ecology, among others (Glimcher et al., 2009). A core assumption behind neuroeconomics is (in common with much if not all contemporary neuroscience) that the brain can be treated as a computational device transforming input in the form of information reaching our sense organs (vision, touch, audition, smell and taste), into output in the form of the generation of behaviour. This transformation is mediated by the billions of highly interconnected neurons (nerve cells) contained in our brains. The main goal of neuroscience is to describe precisely how these neurons act on the incoming sensory information in order to produce a particular output, or, to return to the brain-as-computer analogy, to resolve the algorithms (or mathematical functions) used by the brain to achieve such transformations.